基于CNN的QR码读取无人驾驶飞行器包装的信息
Szu-Yueh Yang1, Hsin-Che Jan1, Chun-Yu Chen1
1Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究介绍了一种用于仓库管理的无人机 (UAV) 系统,使用卷积神经网络 (CNN) 准确扫描包装QR码,成功率高.
科学领域:
- 机器人和自动化 机器人和自动化
- 计算机视觉 计算机视觉
- 供应链管理 供应链管理
背景情况:
- 传统的仓库管理系统在效率和准确性方面面临挑战.
- 手动扫描包装识别码耗时且容易出现错误.
- 整合自主系统可以显著改善库存管理流程.
研究的目的:
- 开发和评估基于无人机 (UAV) 的系统,用于自动化仓库库存管理.
- 通过配备先进传感器和AI的无人机,使包裹上的QR码能够准确地扫描.
- 通过自动化包裹识别来提高仓库运营的效率和可靠性.
主要方法:
- 使用了一架配备飞行控制器,传感器 (光流,超声波) 和摄像头的四旋翼无人机.
- 在无人机稳定中使用比例-整数-导数 (PID) 控制.
- 卷积神经网络 (CNN) 用于识别包放置角度.
- 图像处理技术 (Sobel边缘检测,视角转换) 应用于QR码识别,特别是对不对齐的包裹.
主要成果:
- 拟议的系统实现了高QR码识别率:94%的随机梯度下降 (SGD) 和95%的Adadelta优化.
- 通过使用CNN来准确识别包装放置角度被证明了.
- 在图像处理后,即使包裹不是以90度的角度放置,也可以成功地读取QR码.
结论:
- 基于无人机的仓库管理系统为自动包裹识别提供了可行和高效的解决方案.
- 在动态仓库环境中,CNN和图像处理技术的整合提高了QR码扫描的稳定性.
- 这项技术有可能显著优化仓库运营,减少错误,提高整体生产力.
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